A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks

Yousefpoor, Efat; Barati, Hamid; Barati, Ali

doi:10.1007/s12083-021-01116-3

Cited by 59 publications

(35 citation statements)

References 38 publications

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“…A secured data aggregation scheme was proposed in [ 20 ] to reduce the excessive energy consumption of nodes in WSN. This scheme works in three stages; intra-cluster aggregation; which is responsible for data routing with a cluster; inter-cluster aggregation, which is responsible for data routing between different clusters, and finally data transmission, which handles successful data routing between different clusters.…”

Section: Related Workmentioning

confidence: 99%

Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks

et al. 2022

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Routing Protocol for Low-power and Lossy Networks (RPL), the de facto standard routing protocol for the Internet of Things (IoT) administers the smooth transportation of data packets across the Wireless Sensor Network (WSN). However, the mechanism fails to address the heterogeneous nature of data packets traversing the network, as these packets may carry different classes of data with different priority statuses, some real-time (time-sensitive) while others non-real-time (delay-tolerant). The standard Objective Functions (OFs), used by RPL to create routing paths, treat all classes of data as the same, this practice is not only inefficient but results in poor network performance. In this article, the Prioritized Shortest Path Computation Mechanism (PSPCM) is proposed to resolve the data prioritization of heterogeneous data and inefficient power management issues. The mechanism prioritizes heterogeneous data streaming through the network into various priority classes, based on the priority conveyed by the data. The PSPCM mechanism routes the data through the shortest and power-efficient path from the source to the destination node. PSPCM generates routing paths that exactly meet the need of the prioritized data. It outperformed related mechanisms with an average of 91.49% PDR, and average power consumption of 1.37mW which translates to better battery saving and prolonged operational lifetime while accommodating data with varying priorities.

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Section: Related Workmentioning

confidence: 99%

Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks

et al. 2022

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“…Furthermore, this method has certain disadvantages: (1) The integral-based fitness function is not comprehensive and cannot cover all different cases that two sensor nodes may overlap with each other. However, this method introduce a new idea for calculating the overlap between different sensor nodes and can be improved.…”

Section: Related Workmentioning

confidence: 99%

“…However, these two algorithms also have disadvantages: (1) The fitness function considers only one parameter, namely the overlap between the sensor nodes. However, it can improve with considering more parameters.…”

Section: Related Workmentioning

confidence: 99%

“…Today, wireless sensor networks (WSNs) have been transformed into an attractive research field for many researchers in industry and academia. These networks include a large number of sensor nodes, which are randomly or deterministically deployed in the network environment without any infrastructure [1,2]. Sensor nodes have been tasked to monitor the Region of Interest (RoI).…”

Section: Introductionmentioning

confidence: 99%

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An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

et al. 2021

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Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

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“…For example, health data may include demographic data, images, laboratory results, genomic data, medical records, and data obtained from sensors. Various platforms are used to generate or collect these data samples; for example network servers, electronic health record (EHR), genomic data, personal computers, smartphones, mobile applications, sensors [13,14] and wearable devices [15,16]. Figure 1 represents various data generation resources in healthcare.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks

Cited by 59 publications

References 38 publications

Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks

Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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